Open Multi-Agent Canvas

Name: Open Multi-Agent Canvas
Author: CopilotKit

@CopilotKit

Open Multi-Agent Canvas について

The open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add MCP servers for deep research

#python #typescript #multi-agent #ai-agents #open-canvas #copilotkit #mcp-client

基本情報

カテゴリ

AI とエージェント

ランタイム

node

トランスポート

stdio

公開者

CopilotKit

設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Open Multi-Agent Canvas?

Open Multi-Agent Canvas is an open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation. Built with Next.js, LangGraph, and CopilotKit, it supports travel planning, research, and general-purpose tasks through MCP servers.

How to use Open Multi-Agent Canvas?

Clone the repository, rename example.env to .env in the frontend folder and set NEXT_PUBLIC_CPK_PUBLIC_API_KEY from Copilot Cloud, install dependencies with pnpm i, then build and start with pnpm run build && pnpm run start. Optionally run the MCP Agent backend using Poetry and LangGraph.

Key features of Open Multi-Agent Canvas

Manage multiple agents in one dynamic conversation
Built-in MCP Agent for general-purpose tasks
Connect to custom MCP servers via Stdio or SSE
Public MCP server integration (e.g., mcp.composio.dev)
Supports travel planning and AI research agents
Open-source under MIT License

Use cases of Open Multi-Agent Canvas

Run multiple AI agents simultaneously for travel planning
Conduct web research with specialized researcher agents
Integrate custom MCP tools for task automation
Deploy in collaborative environments for multi-agent workflows

FAQ from Open Multi-Agent Canvas

What is Open Multi-Agent Canvas and how does it differ from single-agent chats?

Open Multi-Agent Canvas allows multiple agents to interact in one conversation, enabling complex multi-step workflows that a single agent cannot handle.

What are the runtime requirements?

You need Node.js, pnpm, and a Copilot Cloud API key. The optional MCP Agent backend requires Python, Poetry, and an OpenAI API key.

Where does my data live?

Data is processed through Copilot Cloud and the configured MCP servers; refer to CopilotKit and MCP documentation for data handling details.

What transport and authentication methods are supported?

MCP servers can be connected via Standard IO (local commands) or SSE (Server-Sent Events). Authentication uses API keys for Copilot Cloud, OpenAI, and LangSmith.

Can I use existing agents with Open Multi-Agent Canvas?

Yes, the project includes reference agents for travel and AI research, and you can add custom agents through the MCP Agent configuration.

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